Discourse Structure in Machine Translation Evaluation

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse analysis. Computational Linguistics, 201

Overview of the SPMRL 2013 shared task: cross-framework evaluation of parsing morphologically rich languages

This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages

International audienceThis paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Ebaluatoia: crowd evaluation of English-Basque machine translation

[EU]Lan honetan Ebaluatoia aurkezten da, eskala handiko ingelesa-euskara itzulpen automatikoko ebaluazio kanpaina, komunitate-elkarlanean oinarritua. Bost sistemaren itzulpen kalitatea konparatzea izan da kanpainaren helburua, zehazki, bi sistema estatistiko, erregeletan oinarritutako bat eta sistema hibrido bat (IXA taldean garatuak) eta Google Translate. Emaitzetan oinarrituta, sistemen sailkapen bat egin dugu, baita etorkizuneko ikerkuntza bideratuko duten zenbait analisi kualitatibo ere, hain zuzen, ebaluazio-bildumako azpi-multzoen analisia, iturburuko esaldien analisi estrukturala eta itzulpenen errore-analisia. Lanak analisi hauen hastapenak aurkezten ditu, etorkizunean zein motatako analisietan sakondu erakutsiko digutenak.[EN]This dissertation reports on the crowd-based large-scale English-Basque machine translation evaluation campaign, Ebaluatoia. This initiative aimed to compare system quality for five machine translation systems: two statistical systems, a rule- based system and a hybrid system developed within the IXA group, and an external system, Google Translate. We have established a ranking of the systems under study and performed qualitative analyses to guide further research. In particular, we have carried out initial subset evaluation, structural analysis and e rror analysis to help identify where we should place future analysis effort

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

In general, advances in translation technology tools have enhanced translation quality significantly. Unfortunately, however, it seems that this is not the case for all language pairs. A concern arises when the users of translation tools want to work between different language families such as Arabic and English. The main problems facing ArabicEnglish translation tools lie in Arabic’s characteristic free word order, richness of word inflection – including orthographic ambiguity – and optionality of diacritics, in addition to a lack of data resources. The aim of this study is to compare the performance of translation memory (TM) and machine translation (MT) systems in translating between Arabic and English.The research evaluates the two systems based on specific criteria relating to needs and expected results. The first part of the thesis evaluates the performance of a set of well-known TM systems when retrieving a segment of text that includes an Arabic linguistic feature. As it is widely known that TM matching metrics are based solely on the use of edit distance string measurements, it was expected that the aforementioned issues would lead to a low match percentage. The second part of the thesis evaluates multiple MT systems that use the mainstream neural machine translation (NMT) approach to translation quality. Due to a lack of training data resources and its rich morphology, it was anticipated that Arabic features would reduce the translation quality of this corpus-based approach. The systems’ output was evaluated using both automatic evaluation metrics including BLEU and hLEPOR, and TAUS human quality ranking criteria for adequacy and fluency.The study employed a black-box testing methodology to experimentally examine the TM systems through a test suite instrument and also to translate Arabic English sentences to collect the MT systems’ output. A translation threshold was used to evaluate the fuzzy matches of TM systems, while an online survey was used to collect participants’ responses to the quality of MT system’s output. The experiments’ input of both systems was extracted from ArabicEnglish corpora, which was examined by means of quantitative data analysis. The results show that, when retrieving translations, the current TM matching metrics are unable to recognise Arabic features and score them appropriately. In terms of automatic translation, MT produced good results for adequacy, especially when translating from Arabic to English, but the systems’ output appeared to need post-editing for fluency. Moreover, when retrievingfrom Arabic, it was found that short sentences were handled much better by MT than by TM. The findings may be given as recommendations to software developers

New perspectives on cohesion and coherence: Implications for translation

The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation

New perspectives on cohesion and coherence: Implications for translation

The contributions to this volume investigate relations of cohesion and coherence as well as instantiations of discourse phenomena and their interaction with information structure in multilingual contexts. Some contributions concentrate on procedures to analyze cohesion and coherence from a corpus-linguistic perspective. Others have a particular focus on textual cohesion in parallel corpora that include both originals and translated texts. Additionally, the papers in the volume discuss the nature of cohesion and coherence with implications for human and machine translation.The contributors are experts on discourse phenomena and textuality who address these issues from an empirical perspective. The chapters in this volume are grounded in the latest research making this book useful to both experts of discourse studies and computational linguistics, as well as advanced students with an interest in these disciplines. We hope that this volume will serve as a catalyst to other researchers and will facilitate further advances in the development of cost-effective annotation procedures, the application of statistical techniques for the analysis of linguistic phenomena and the elaboration of new methods for data interpretation in multilingual corpus linguistics and machine translation